Measuring the Elastic Behavior of Knitted Fabrics
ORAL
Abstract
Knitting is an ancient technology, with ubiquitous modern applications, that uses non-elastic yarn to create highly elastic fabric through interlocking loops. Recent work has been done to create a model of the simplest weft-knitted fabric stitch patterns in order to predict the bulk elastic behavior. This model incorporates the energy stored primarily in the bending and compressing of the yarn, with variation depending on the pattern of the two basic stitches: knit and purl. Stress-strain measurements provide a measure of the elastic behavior, which has been shown to depend on these stitch-level energies, but also the stitch pattern.
We extend this work by measuring how well the model predicts the variation in elasticity with the gauge, or length of yarn per stitch. We present data for cotton yarn samples knit in stockinette stitch and find that the model accurately predicts the relative dependence of stress-strain curves on gauge, but consistently underestimates the absolute stress due to the 2D behavior of the samples not included in the 1D model. We demonstrate high repeatability in our measurements by controlling yarn tension while knitting, consistent blocking procedures, and eliminating cast-on and bind-off edge effects.
We extend this work by measuring how well the model predicts the variation in elasticity with the gauge, or length of yarn per stitch. We present data for cotton yarn samples knit in stockinette stitch and find that the model accurately predicts the relative dependence of stress-strain curves on gauge, but consistently underestimates the absolute stress due to the 2D behavior of the samples not included in the 1D model. We demonstrate high repeatability in our measurements by controlling yarn tension while knitting, consistent blocking procedures, and eliminating cast-on and bind-off edge effects.
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Presenters
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Etienne Gagnon
Franklin & Marshall College
Authors
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Etienne Gagnon
Franklin & Marshall College
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Amy L Lytle
Franklin & Marshall College
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Jacob Macchi
Franklin & Marshall College
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Liyan Chen
Franklin & Marshall College